Polymer physical property prediction device, recording medium, and polymer physical property prediction method

ABSTRACT

A polymer physical property prediction device includes a processor, and a memory storing program instructions that cause the processor to read a structural unit from a storage unit and use the structural unit to calculate numbers each indicating how many substructures are in a polymer, the polymer being formed of repetition of the structural unit, calculate a number indicating how many atoms are in the structural unit, calculate number densities of the substructures from the numbers of substructures and the number of atoms in the structural unit, construct a regression model that predicts a physical property value by using an experimental value of a physical property of the polymer and the number densities of the substructures, input a polymer structure of which the physical property value is to be predicted, and predict the physical property value corresponding to the input polymer structure by using the regression model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of InternationalApplication No. PCT/JP2019/008704 filed on Mar. 5, 2019, which is basedupon and claims priority to Japanese Priority Application No.2018-043171 filed on Mar. 9, 2018, the entire contents of which arehereby incorporated by reference.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a device that predicts the physicalproperties of a polymer (hereinafter “polymer physical propertyprediction device”, a recording medium, and a polymer physical propertyprediction method.

2. Description of the Related Art

In recent years, machine learning has been used to predict physicalproperties of organic compounds such as polymers by using a computer.

Conventional techniques for predicting. physical properties of polymersuse various structure descriptors configured by humans to predictphysical properties. Techniques for mechanically identifyingsubstructure patterns of molecules and calculating structure descriptorsbased on algorithms have also been proposed.

Conventionally, it is necessary for a human to determine a structuralpattern, such as the number of carbon atoms, that serves as a structuredescriptor of a polymer in advance. With respect to this, there is amethod for automatically extracting substructures of low-molecularweight organic molecules, but there is a problem that the accuracy ofpredicting the physical properties of a polymer is low. Thus, the methodhas not been applied to polymer structures.

RELATED-ART DOCUMENTS Patent Documents

[Patent Document 1] Japanese National Publication of InternationalPatent Application No. 2004-514902

Non-Patent Document

[Non-Patent Document 1] David Rogers et al., “Extended-ConnectivityFingerprints”, American Chemical Society, J. Chem. Info. Model., Vol.50, No. 5, 2010, pp. 742-754.

SUMMARY OF THE INVENTION

According to one aspect, a polymer physical property prediction deviceincludes a processor, and a memory storing program instructions thatcause the processor to read a structural unit from a storage unit anduse the structural unit to calculate numbers each indicating how manysubstructures are in a polymer, the polymer being formed of repetitionof the structural unit, calculate a number indicating how many atoms arein the structural unit, calculate number densities of the substructuresfrom the numbers of substructures and the number of atoms in thestructural unit, construct a regression model that predicts a physicalproperty value by using an experimental value of a physical property ofthe polymer and the number densities of the substructures, theexperimental value being obtained from the storage unit, input a polymerstructure of which the physical property value is to be predicted, andpredict the physical property value corresponding to the input polymerstructure by using the regression model.

A means to solve the above-described problem can be a program or amethod for predicting the physical properties of a polymer.

According to at least one embodiment of the present invention, thephysical properties of a polymer can be accurately predicted.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing illustrating an example of a system configurationaccording to the present embodiment;

FIG. 2 is a drawing illustrating an example of a hardware configurationof a physical property prediction device;

FIG. 3 is a drawing illustrating a first functional configurationexample of the physical property prediction device;

FIG. 4 is a drawing illustrating an example of a data configuration of apolymer structure and physical property DB;

FIG. 5 is a drawing for describing an overview of a prediction processaccording to the present embodiment;

FIG. 6 is a drawing illustrating an example of the number density;

FIG. 7 is a drawing for describing a difference caused when replicationof a repeating unit is performed and no replication of a repeating unitis performed;

FIG. 8A is a flowchart for describing the prediction process;

FIG. 8B is a flowchart for describing the prediction process;

FIG. 9 is a drawing illustrating an example of a data configuration of aregression model management table;

FIG. 10 is a drawing illustrating a second functional configurationexample of the physical property prediction device;

FIG. 11 is a drawing illustrating a third functional configurationexample of the physical property prediction device;

FIG. 12 is a drawing illustrating a fourth functional configurationexample of the physical property prediction device;

FIG. 13 is a drawing illustrating a functional configuration example ofa physical property prediction device based on an existing technique;

FIG. 14 is a drawing illustrating an example of evaluation results;

FIG. 15 is a drawing illustrating an example of evaluation results; and

FIG. 16 is a drawing illustrating an example of a manufacturing methodfor manufacturing a polymer.

DESCRIPTION OF THE EMBODIMENTS

In the following, an embodiment of the present invention will bedescribed based on the drawings. First, an example of a systemconfiguration according to the present embodiment will be described.FIG. 1 is a drawing illustrating the example of the system configurationaccording to the present embodiment In FIG. 1, a system 1000 includes aphysical property prediction device 100 and multiple terminals 3, andeach of the terminals can be connected through a network 2.

The terminal 3 is an information processing terminal used by a user, isconnected to the physical property prediction device 100, and utilizesprocessing performed by a prediction processing unit 40.

The physical property prediction device 100 is an information processingdevice including the prediction processing unit 40 that predicts aphysical property of a polymer. In the physical property predictiondevice 100, the prediction processing unit 40 performs machine learningusing a structure of the polymer and data indicating the physicalproperty of the polymer that are provided in advance to generate aregression model. In response to receiving a polymer structure 6 a fromthe terminal 3, a physical property value is predicted using thegenerated regression model with respect to the received polymerstructure 6 a, and a predicted physical property value 6 b is sent tothe terminal 3.

A configuration of the system 1000 is not limited to the example ofFIG. 1. As an example, the physical property prediction device 100 maybe utilized in a stand-alone manner. In this case, when the polymerstructure 6 a is input by a user operation of an input device 14 (FIG.2), the physical property value 6 b is output to a display device 15(FIG. 2).

The physical property prediction device 100 according to the presentembodiment has a hardware configuration as illustrated in FIG. 2. FIG. 2is a drawing illustrating an example of the hardware configuration ofthe physical property prediction device. In FIG. 2, the physicalproperty prediction device 100 is an information processing devicecontrolled by a computer and includes a central processing unit (CPU)11, a main storage device 12, an auxiliary storage device 13, the inputdevice 14, the display device 15, a communication interface (I/F) 17,and a drive device 18, which are connected to a bus B.

The CPU 11 corresponds to a processor that controls the physicalproperty prediction device 100 according to a program stored in the mainstorage device 12. As the main storage device 12, a random access memory(RAM) and a read only memory (ROM) are used to store or temporarilystore a program executed by the CPU 11, data necessary for processing bythe CPU 11, and data obtained by processing by the CPU 11, for example.

A hard disk drive (HDD) is used for the auxiliary storage device 13, forexample, to store data such as a program for executing variousprocessing. A portion of the program stored in the auxiliary storagedevice 13 is loaded into the main storage device 12 and is executed bythe CPU 11 to achieve various processing. Hereinafter, the main storagedevice 12, the auxiliary storage device 13, and an external storagedevice accessible by the physical property prediction device 100 will becollectively referred to as a storage unit 130.

The input device 14 includes a mouse and a keyboard and is used by auser to input various information required for processing by thephysical property prediction device 100. The display device 15 displaysvarious necessary information under control of the CPU 11. The inputdevice 14 and the display device 15 may be a user interface, such as anintegrated touch panel. The communication I/F 17 performs communicationthrough a network, such as a wired or wireless network. Thecommunication by the communication I/F 17 is not limited to the wired orwireless communication. The drive device 18 interfaces a recordingmedium 19 set in the drive device 18 (e.g., a compact disc read-onlymemory (CD-ROM)) with the physical property prediction device 100.

A program for implementing the processing performed by the physicalproperty prediction device 100 is provided to the physical propertyprediction device 100 through the recording medium 19, such as a CD-ROM.A program for implementing various processing according to the presentembodiment, which will be described later, is stored in the recordingmedium 19, and the program stored in the recording medium 19 isinstalled in the physical property prediction device 100 through thedrive device 18. The installed program can be executed by the physicalproperty prediction device 100.

The recording medium 19 for storing the program is not limited to aCD-ROM, but may be one or more non-transitory and tangible media havinga computer readable data structure. As a computer readable recordingmedium in addition to a CD-ROM, a portable storage medium, such as adigital versatile disk (DVD) and a USB memory, or a semiconductormemory, such as a flash memory, may be used.

[First Functional Configuration Example]

FIG. 3 is a drawing illustrating a first functional configurationexample of the physical property prediction device. In FIG. 3, thephysical property prediction device 100 includes the predictionprocessing unit 40. The prediction processing unit 40 corresponds to aprediction process performed by the CPU 11 executing a correspondingprogram.

The storage unit 130 stores a polymer structure and physical property DB31, a model structure 33 a, the number of substructures 33 b, the numberof atoms 33 c, a substructure number density 33 d, an importantsubstructure 33 e, a regression model 33 f, a polymer structure 6 a, aphysical property value 6 b, for example.

The prediction processing unit 40 obtains the regression model 33 f forpredicting the physical properties of the polymer structure 6 aspecified by a user by performing machine learning, predicts thephysical properties of the polymer structure 6 a by using the obtainedregression model 33 f, and outputs the obtained physical property value6 b. The prediction processing unit 40 includes a model generator 41 anda physical property prediction unit 45 as processing units.

The model generator 41 learns the physical property of the polymerstructure in which a structural unit is repeatedly replicated and linkedusing the polymer structure and physical property DB31 and determinesthe regression model 33 f. A structural unit forming a polymer byrepetition is called a “repeating unit”. In the polymer structure andphysical property DB 31, a repeating unit and an experimental value of aphysical property experimentally obtained in advance are stored inassociation with each polymer.

The model generator 41 includes a model structure generator 43 a, asubstructure number calculating unit 43 b, an atom calculating unit 43c, a substructure number density calculating unit 43 d, a dimensionreduction unit 43 e, and a regression model construction unit 43 f asprocessing units. Either or both of the model structure generator 43 aand the dimension reduction unit 43 e can be removed.

The model structure generator 43 a obtains the repeating unit from thepolymer structure and physical property DB 31 and repeats the process ofreplicating and linking the obtained repeating unit a predeterminednumber of times to generate the model structure 33 a. The repeating unitincludes a substructure. The repeating unit may also be an entire of asubstructure. The repeating unit are linked multiple times to representa polymer.

The generated model structure 33 a is stored in the storage unit 130.The repeating unit and the model structure 33 a may be represented in apredetermined form. The first functional configuration example describesthe repeating unit and the model structure 33 a in the simplifiedmolecular input line entry syntax (SMILES) notation, but the notation isnot limited to this.

The substructure number calculating unit 43 b calculates the number ofsubstructures 33 b based on the model structure 33 a. The numbers ofsubstructures 33 b are represented by an n-dimensional vector and storedin the storage 130. The atom calculating unit 43 c calculates the numberof atoms 33 c based on the model structure 33 a. The obtained number ofatoms 33 c is stored in the storage unit 130.

The substructure number density calculating unit 43 d calculates thesubstructure number density 33 d by using the numbers of substructures33 b and the number of atoms 33 c. The substructure number density 33 dcan be obtained by the following equation:

substructure number density=number of substructures÷number of atoms

The obtained substructure number densities 33 d are represented by avector of the same n-dimensional vector as the vector of thesubstructure number 33 b and are stored in the storage unit 130.

The dimension reduction unit 43 e constructs a temporal regression modelby using the substructure number density 33 d and the experimentalvalue, and reduces the dimensions of the temporal regression model basedon a predetermined dimension reduction method to obtain the importantsubstructure 33 e. The important substructure 33 e is stored in thestorage unit 130. The important substructure 33 e is data specifying anelement (e.g., a vector) that will contribute to the prediction accuracyof the regression model 33 f to be constructed in the substructurenumber density 33 d.

For the dimension reduction method, a procedure corresponding to aregression analysis method to be used is determined in advance. Forexample, when the lasso regression is used, the dimensions can bereduced by removing a coefficient term of 0. When the random forestregression is used, the dimensions can be reduced by calculating theimportance of explanatory variables and excluding variables that havethe importance lower than or equal to a threshold. In the stepwisemethod, the dimensions can be reduced by searching for combinations ofexplanatory variables having highest prediction accuracy by adding orreducing an explanatory variable one by one when a temporal regressionmodel is constructed.

The regression model construction unit 43 f constructs the regressionmodel 33 f by performing a regression analysis based on the importantsubstructure 33 e obtained by the dimension reduction unit 43 e by usingthe substructure number density 33 d and the experimental value. Theregression model 33 f is stored in the storage unit 130.

In the dimension reduction unit 43 e and the regression modelconstruction unit 43 f, the experimental value can be obtained from thepolymer structure and physical property DB 31. The regression model 33 fobtained by the regression model construction unit 43 f is expressed inthe following equation.

y=f(x)

By substituting the substructure number density 33 d for a variable x, apredicted value y of the physical property is obtained.

To the regression analysis for obtaining the temporal regression modeland the regression model 33 f, any one of Lasso regression, randomforest regression, a stepwise method, partial linear regression, ridgeregression, kernel ridge regression, support vector regression, decisiontree regression, Gaussian process regression, and so on can be applied.As an example, a temporal regression model can be obtained by the Lassoregression, and the regression model 33 f can be obtained by theGaussian process regression.

Next, the physical property prediction unit 45 predicts the physicalproperties of the polymer based on the polymer structure 6 a by usingthe regression model 33 f in response to an input of the polymerstructure 6 a specified by a user. The polymer structure 6 a may specifythe repeating unit by the SMILES notation or the like. The physicalproperty prediction unit 45 includes a polymer structure input unit 47 aand a polymer physical property prediction unit 47 b as processingunits.

The polymer structure input unit 47 a inputs the polymer structure 6 aspecified by a user and the polymer structure 6 a is stored in thestorage unit 130. The polymer structure input unit 47 a may input thepolymer structure 6 a by the communication I/F 17 receiving the polymerstructure 6 a through the network 2 or may input the polymer structure 6a in accordance with a user's specification by the input device 14 ofthe property prediction device 100.

The polymer physical property prediction unit 47 b obtains the modelstructure, the numbers of substructures, and the number of atoms, fromthe polymer structure 6 a, to calculate the substructure number density,and predicts the physical property value by using the regression model33 f stored in the storage unit 130. The method of obtaining the modelstructure, the numbers of substructures, the number of atoms, and thesubstructure number density based on the polymer structure 6 a issimilar to the method of the model generator 41.

The predicted physical property value 6 b is output to the storage unit130. When the polymer structure 6 a is specified through the network 2,the obtained physical property value 6 b is sent to the terminal 3 ofthe user and displayed. When the polymer structure 6 a is specified by auser operation at the physical property prediction device 100, theobtained physical property value 6 b is displayed on the display device15.

FIG. 4 is a drawing illustrating an example of a data configuration ofthe polymer structure and physical property DB. In FIG. 4, the polymerstructure and physical property DB 31 is a database that manages therepeating unit and experimental values of various physical properties,such as a refractive index, for each polymer, and includes items such asa polymer name, the repeating unit, the refractive index, and a glasstransition point.

The polymer name indicates specific information specifying the polymerstructure. An example in which the repeating unit is represented by theSMILES notation is used for the following description, but the notationof the repeating unit is not limited to the SMILES notation. Therefractive index, the glass transition point, and the like are examplesof various physical properties of the polymer experimentally obtained inadvance, and the experimental values are shown for each name of thephysical property.

In this example, a repeating unit “*CC*” is associated with a polymername “poly(ethylene)” and the experimental values, such as therefractive index “1.472”, are shown. Similarly, an experimental value ofeach physical property is shown for another polymer.

In the model generator 41, referenced data, generated data, and the likemay be managed in a work table 32. The work table 32 is a table in whichcalculated values are stored for each repeating unit of the polymer ofwhich the substructure number density 33 d is obtained, and includesitems such as the polymer name, the repeating unit, the number of atoms,and the substructure number density.

The polymer name indicates a polymer name registered in the polymerstructure and physical property DB 31. The number of substructuresindicates the number of substructures 33 b obtained by the substructurenumber calculating unit 43 b. The number of atoms indicates the numberof atoms 33 c obtained by the atom calculating unit 43 c. Thesubstructure number density indicates the substructure number density 33d obtained by the substructure number density calculating unit 43 d.

The substructure number density of the work table 32 and theexperimental values of the physical property of the polymer structureand physical property DB 31 are used for the machine learning by theregression analysis. Thus, the work table 32 may be associated with thepolymer structure and physical property DB 31 by the polymer name. Here,the data configuration example of the polymer structure and physicalproperty DB 31 and the work table 32 illustrated in FIG. 4 is only oneexample, but is not limited to this example.

An outline of an entire prediction process performed by the predictionprocessing unit 40 will be described using an example of the repeatingunit. FIG. 5 is a drawing for describing the overview of the predictionprocess according to the present embodiment. As illustrated in FIG. 5,in the model generator 41 of the prediction processing unit 40, themodel structure generator 43 a obtains the repeating unit from thepolymer structure and physical property DB 31 (as described in (a) ofFIG. 5). For example, a repeating unit “*CCO*” is obtained. In thefollowing description, “*” indicating a start and an end of a certainstructure in the SMILES notation will be omitted.

The model structure generator 43 a uses the obtained substructure as arepeating unit, and replicates the repeating unit to generate the modelstructure 33 a (as described in (b) of FIG. 5). In this example, themodel structure generator 43 a repeats replicating the repeating unit“CCO” to the right a predetermined number of times, to generate themodel structure 33 a “CCOCOCOCCCOCCCOCCCOCCCOCCCOCCO”, in which therepeating unit “CCO” are linked multiple times, in the memory unit 130.

The substructure number calculating unit 43 b generates a substructurenumber vector x₀ (i.e., the numbers of substructures 33 b) of thestructure model 33 a (as described in (c) in FIG. 5). The substructurenumber vector x₀ is represented in n dimensions by a structuredescriptor, such as extended circular finger print (ECFP), atom pair,topological torsion, and neural finger print. In the first functionalconfiguration example, a structure descriptor represented by the numberof C atoms, the number of O atoms, the number of COCs, or the like isused for the description. However, the structure descriptor is notlimited to this.

In the structure model “CCOCCCOCCCOCCCOCCCOCCCOCCO”, an n-dimensionalsubstructure number vector x₀, which consists of the number of C atoms“20”, the number of O atoms “10”, . . . , and the number of COCs “9”, isgenerated. A structure assigned to each vector, such as “C”, “O”, “COC”,indicates the type of the substructure of the polymer. Here, n is anarbitrary natural number, and is usually about 256 to 2048.

The atom calculating unit 43 c calculates the number of atoms includedin the model structure 33 a (as described in (d) of FIG. 5). In thestructure model “CCOCOCOCOCCCOCCCOCCCOCCCO”, the number of atoms is“30”.

The substructure number density calculating unit 43 d divides thesubstructure number vector x₀ (i.e., the numbers of substructures 33 b)obtained by the substructure number calculating unit 43 b in (c) of FIG.5 by the number of atoms 33 c obtained by the atom calculating unit 43 cin (d) of FIG. 5 to generate the substructure number density vector x(i.e., the substructure number densities 33 d) in (e) of FIG. 5.

Specifically, in the structure model “CCOCCCOCCOCCOCCCOCCCOCCCOCCCOCCO”,each value of the substructure number vector x₀ is divided by the numberof atoms “30” to obtain the substructure number density vector xrepresenting the C atom number density “⅔ (=20/30)”, the O atom numberdensity “⅓ (=10/30)”, . . . , and the COC density “ 9/30.” The number ofdimensions of the substructure number density vector x is n, which isthe same number as the number of dimensions of the substructure numbervector x₀.

For each record of the polymer structure and physical property DB 31,(a) of FIG. 5 to (e) of FIG. 5 are repeated. The number of substructures33 b, the number of atoms 33 c, and the substructure number density 33 dthat are obtained may be stored in the work table 32 in association withthe polymer name.

Before constructing the regression model 33 f, a temporal regressionmodel is constructed by the dimension reduction unit 43 e and thedimension reduction is performed (in (f) of FIG. 5). This can reduce ndimensions to n′ dimensions (n′<n). By the dimension reduction, theimportant substructure 33 e that contributes to the prediction accuracyis determined. Here, n′ is a natural number smaller than n, and isusually about 16 to 128.

The regression model construction unit 43 f constructs the regressionmodel 33 f used in predicting the physical property value based on theimportant substructure 33 e (in (g) of FIG. 5). The regression model 33f, whose dimensions are reduced to several tens dimensions, is obtained.The regression model 33 f is expressed by the following equation (1)that obtains the predicted value y of the physical property of thepolymer by the substructure number density vector x (i.e., thesubstructure number densities 33 d).

y=f(x)  (1)

When dimensions are not reduced, the number of substructure numberdensity vector x is n, which is the number of dimensions. But afterdimensions are reduced, the dimensions of the substructure numberdensity vector x are n′. Because the predicted values are calculatedusing only the dimensions contributing to the prediction accuracy byusing the important substructure 33 e, the physical property predictionprocess can be efficiently performed.

The above-described process from (a) to (e) in FIG. 5 corresponds to themodel generating process of the model generator 41. After the modelgenerating process has been performed, in response to receiving theinput of the polymer structure 6 a desired to be processed from theuser, the physical property prediction process is performed by thephysical property prediction unit 45.

In response to the input of the polymer structure A of which thephysical property is to be obtained, the physical property predictionunit 45 generates the substructure number density vector x_(A), andpredicts the physical property value y_(A) (in (h) of FIG. 5). Thepolymer structure input unit 47 a inputs the polymer structure A, andthe polymer physical property prediction unit 47 b predicts the physicalproperty value y_(A) by using the regression model 33 f.

The physical property value y_(A) with respect to the polymer structureA desired to be processed can be obtained by substituting thesubstructure number density x_(A) in the above equation (1).

y _(A) =f(x _(A))  (2)

If the regression model 33 f is for only one physical property, thevalue of a predetermined physical property is predicted. If theregression model 33 f is obtained with respect to each of the variousphysical properties, the user may specify the polymer structure A andall physical properties or a specific physical property. In this case,the polymer physical property prediction unit 47 b predicts the physicalproperty value 6 b by using the regression model 33 f corresponding toeach physical property and the important substructure 33 e.

The process of calculating the numbers of substructures 33 b in (c) ofFIG. 5 and the process of calculating the number of atoms in (d) of FIG.5 may be performed in any order. After calculating the number of atoms33 c is performed, the numbers of substructures 33 b may be calculated,or the number of atoms 33 c and the numbers of substructures 33 b may becalculated in parallel. In the present embodiment, a “predicted value”of the physical property and a predicted “physical property value” areidentical.

In the first functional configuration example, the substructure numberdensity calculating unit 43 d of the prediction processing unit 40 canrecognize whether polymer structures are identical. FIG. 6 is a drawingillustrating an example of the number density. In FIG. 6, the number ofcarbons of a polyethylene oxide is illustrated as an example.

Referring to FIG. 6, when a structure A and a structure B are compared,a notation of the structure B is longer and different from a notation ofthe structure A. However, polymers represented by the structure A andthe structure B are identical.

In an existing technique in which the number of substructures is used asa structure descriptor, the number of carbons of the structure A is “2,”the number of carbons of the structure B is “4”. Since the numbers ofcarbons of the structure A and the structure B are different, thepolymers are mistakenly recognized as being different. This exampleindicates that when polymers having different lengths of the repeatingunit are compared, the number of substructures cannot appropriatelycapture structural features of the polymer.

In the first functional configuration example, the substructure numberdensity calculating unit 43 d obtains the same substructure numberdensity (carbon number density) “⅔” with respect to the structure A andthe structure B. Thus, the dimension reduction unit 43 e and theregression model construction unit 43 f can correctly recognize that thestructure A and the structure B represent the same polymer. Based onthese examples, the inventors have found that by using the substructurenumber density 33 d, polymers having different lengths of the repeatingunit can be treated equivalently.

The inventors have also found that replicating and linking the repeatingunit can improve the existing technique having low prediction accuracy.A difference between when replication is performed and when noreplication is performed is described in FIG. 7. FIG. 7 is a drawing fordescribing the difference between when replication of the repeating unitis performed and when no replication is performed. In FIG. 7, apolyethylene oxide is used as an example. A repeating unit whenreplication is performed and a repeating unit when no replication isperformed are identical.

When the repeating unit is not replicated, there is no substructure“COC”, which is expected to exist, at both ends of the repeating unit.Thus, COC number density is “0”. With respect to this, in the firstfunctional configuration example, the repeating unit is replicated andthe right side of the repeating unit is linked to an oxygen atom on theleft side of the next repeating unit. Two repeating units causes thesubstructure “COC” to appear and “⅙” is obtained with respect to thenumber density of “COC”, which is expected to exist. In the firstfunctional configuration example, the regression model 33 f is obtainedusing the substructure number density 33 d obtained in such a manner,thereby improving the prediction accuracy of the existing technique.

As described above, the prediction process is performed by theprediction processing unit 40 as illustrated in FIGS. 8A and 8B. FIGS.8A and 8B are flowcharts for describing the prediction process. First, amodel generating process P51 is performed by the model generator 41.

In step S511, in the model generator 41, the model structure generator43 a selects and reads one record from the polymer structure andphysical property DB 31, and obtains the repeating unit and theexperimental value. In step S512, the model structure generator 43 arepeats an operation of replicating and linking the obtained repeatingunit a predetermined number of times to generate the model structure 33a.

In step S513, the substructure number calculating unit 43 b calculatesthe number of substructures 33 b of the model structure 33 a by using apredetermined structure descriptor (e.g., ECFP). In step S514, the atomcalculating unit 43 c calculates the number of atoms 33 c of the modelstructure 33 a. Step S513 may be performed after step S514 has beenperformed. Alternatively, steps S513 and S514 may be performed inparallel.

In step S515, the substructure number density calculating unit 43 dcalculates the substructure number density 33 d by dividing the numberof substructures 33 b by the number of atoms 33 c. As described above,the numbers of substructures 33 b and the substructure number densities33 d are represented by an n-dimensional vector.

In step S516, the model generator 41 determines whether all records ofthe polymer structure and the physical property DB 31 have beencompleted. When all records have not been completed (NO in step S516),the model generator 41 returns to step S511 and performs a processsimilar to the process described above.

When all records have been completed (YES in step S516), the dimensionreduction unit 43 e performs a first regression analysis for obtaining atemporal regression model f′ by using the substructure number density 33d corresponding to the important substructure 33 e as a variable x′ andan experimental value as y′. In step S517, the dimension reduction unit43 e reduces the dimensions of the temporal regression model f′ by apredetermined dimension reduction method and obtains the importantsubstructure 33 e.

Next, the regression model construction unit 43 f performs a secondregression analysis. In step S518, the regression model constructionunit 43 f obtains the regression model f by using the substructurenumber density 33 d corresponding to the important substructure 33 e asthe variable x and the experimental value as y. The experimental value yis a value determined by the polymer name and the physical property inthe polymer structure and physical property DB 31. The regression modelf, that is, the regression model 33 f, is stored in the storage unit130.

When the regression model f is obtained for all or some of the physicalproperties registered in the polymer structure and physical property DB31, an experimental value may be obtained for each physical property,and steps S517 to S518 may be repeated.

Subsequently, the physical property prediction process P55 is performedby the physical property prediction unit 45. When the polymer structureinput unit 47 a receives the polymer structure 6 a in the predictionprocessing unit 40 (step S521), the model structure, the number ofsubstructures, the number of atoms, and the substructure number densityare obtained using the polymer structure 6 a as the repeating unit (stepS522). The model structure, the number of substructures, the number ofatoms, and the substructure number density of the polymer structure 6 aare obtained by performing a process similar to the process from stepS512 to step S515 in the model generating process P51.

In step S524, the polymer physical property prediction unit 47 bpredicts the physical property value 6 b based on the importantsubstructure 33 e obtained in step S517 by using the regression model f(i.e., the regression model 33 f stored in the storage unit 130)obtained in step S518 by reducing the dimensions of the substructurenumber density of the polymer structure 6 a. The obtained physicalproperty value 6 b is sent to the terminal 3 or displayed on the displaydevice 15.

When the regression model 33 f corresponding to each of multiplephysical properties is generated, it is desirable to manage thegenerated regression models 33 f in a table as illustrated in FIG. 9.FIG. 9 is a drawing illustrating an example of a data configuration of aregression model management table.

In FIG. 9, a regression model management table 33 k is a table formanaging the regression model 33 f for each physical property, andincludes items such as the physical property, the regression model, andthe important substructure. The physical property represents a name forspecifying the physical property, such as the refractive index, and theglass transition point. The regression model represents the regressionmodel 33 f corresponding to the physical property, constructed in theregression model construction unit 43 f.

The important substructure specifies a valid vector of the vector of thesubstructure number density 33 d, obtained after dimension reduction(i.e., a valid vector after dimension reduction) for using theregression model 33 f. When the dimension reduction is omitted, the itemof the important substructure is omitted.

In the physical property prediction unit 45, the polymer physicalproperty prediction unit 47 b obtains the regression model 33 f and theimportant substructure 33 e that are corresponding to the physicalproperty to be predicted from the regression model management table 33k.

The inventors have verified the prediction accuracy of variousfunctional configurations described below. According to verificationresults, the first functional configuration example described above canimprove the prediction accuracy the most. Further, the inventors haveverified that the prediction accuracy can be sufficiently improved evenin a configuration in which either or both of the model structuregenerator 43 a and the dimension reduction unit 43 e are removed.

First, other examples of the functional configuration in which theprediction accuracy is verified will be described below. In thefollowing description, a second functional configuration example is afunctional configuration in which both of the model structure generator43 a and the dimension reduction unit 43 e are removed, a thirdfunctional configuration example is a functional configuration in whichthe model structure generator 43 a is removed, and a fourth functionalconfiguration example is a functional configuration. in which thedimension reduction unit 43 e is removed. In the second functionalconfiguration example to the fourth functional configuration example,processing units similar to the processing units of the first functionalconfiguration example illustrated in FIG. 3 are referenced by the samereference numerals, and the detailed description is omitted.

[Second Functional Configuration Example]

FIG. 10 is a drawing illustrating the second functional configurationexample of the physical property prediction device. In FIG. 10, in thesecond functional configuration example, the model structure generator43 a and the dimension reduction unit 43 e are removed from the firstfunctional configuration example illustrated in FIG. 3. Thus, the modelstructure 33 a and the important substructure 33 e are not generated.

In the model generator 41, the substructure number calculating unit 43 band the atom calculating unit 43 c obtains the number of substructures33 b and the number of atoms 33 c by obtaining the repeating unit fromthe polymer structure and physical property DB 31.

Also in the second functional configuration example, the substructurenumber density calculating unit 43 d calculates the substructure numberdensity 33 d. Then, the regression model construction unit 43 f obtainsthe regression model 33 f by using the calculated substructure numberdensity 33 d and the experimental value of the physical property.

In the physical property prediction unit 45, when the polymer structureinput unit 47 a inputs the polymer structure 6 a, the polymer physicalproperty prediction unit 47 b calculates the number of substructures andthe number of atoms by using the polymer structure 6 a without creatingthe model structure 33 a. The polymer physical property prediction unit47 b obtains the substructure number density by dividing the obtainednumber of substructures by the number of atoms, and uses thesubstructure number density 33 d to obtain the property value 6 b by theregression model 33 f.

In the second functional configuration example, the regression analysisis performed only once. The number of dimensions of the regression model33 f is the same number as the number of dimensions of the substructurenumber densities 33 d, and the important substructure 33 e is notdetermined. However, if the values of the substructure number density 33d are identical, even polymers with different lengths of the repeatingunit can be treated equivalently, and the prediction accuracy can besufficiently improved.

[Third Function Configuration Example]

FIG. 11 is a drawing illustrating the third functional configurationexample of the physical property prediction device. In FIG. 11, in thethird functional configuration example, the model structure generator 43a is removed from the first functional configuration example illustratedin FIG. 3. Thus, the model structure 33 a is not generated.

In a manner similar to the second functional configuration example, inthe model generator 41, the substructure number calculating unit 43 band the atom calculating unit 43 c obtain the repeating unit from thepolymer structure and physical property DB 31 to obtain the number ofsubstructures 33 b and the number of atoms 33 c. The substructure numberdensity calculating unit 43 d obtains the substructure number density 33d. Then, the dimension reduction unit 43 e obtains the importantsubstructure 33 e and reduces the dimensions of the substructure numberdensities 33 d, and the regression model construction unit 43 f outputsthe regression model 33 f.

In the physical property prediction unit 45, when the polymer structureinput unit 47 a inputs the polymer structure 6 a, the polymer physicalproperty prediction unit 47 b calculates the number of substructures andthe number of atoms by using the polymer structure 6 a withoutgenerating the model structure 33 a. Then, the polymer physical,property prediction unit 47 b obtains the substructure number density bydividing the obtained number of substructures by the number of atoms,reduces the number of dimensions of the substructure number densitiesbased on the substructure number density 33 d, and outputs the propertyvalue 6 b by using the regression model 33 f.

In the third functional configuration example, when the values of thesubstructure number density 33 d are identical, even polymers withdifferent lengths of the repeating unit can be equivalently treated.Although the model structure 33 a is not generated, the importantsubstructure 33 e is determined by the dimension reduction unit 43 e,thereby improving the prediction accuracy of the regression model 33 fgenerated by the regression model construction unit 43 f.

[Fourth Functional Configuration Example]

FIG. 12 is a drawing illustrating the fourth functional configurationexample of the physical property prediction device. In FIG. 12, in thefourth functional configuration example, the dimension reduction unit 43e is removed from the first functional configuration example illustratedin FIG. 3. Thus, the important substructure 33 e is not generated.

In the model generator 41, the model, structure generator 43 a generatesthe model structure 33 a, and the substructure number calculating unit43 b and the atom calculating unit 43 c obtain the number ofsubstructures 33 b and the number of atoms 33 c based on the modelstructure 33 a.

In the physical property prediction unit 45, when the polymer structureinput unit 47 a inputs the polymer structure 6 a, the polymer physicalproperty prediction unit 47 b generates the model structure 33 a andcalculates the number of substructures and the number of atoms by usingthe model structure 33 a. Then, the polymer physical property predictionunit 47 b obtains the substructure number density by dividing theobtained number of substructures by the number of atoms, and uses thesubstructure number density 33 d to obtain the physical property value 6b by the regression model 33 f. The dimensions of the substructurenumber densities obtained from polymer structure 6 a are not reduced.

In the fourth functional configuration, when the values of thesubstructure number density 33 d are identical, even polymers withdifferent lengths of the repeating unit can be equivalently treated.Although the important substructure 33 e is not generated, the modelstructure 33 a and the substructure number density 33 d can improve theprediction accuracy of the regression model 33 f generated by theregression model construction unit 43 f.

Next, a functional configuration example based on the existing techniquewill be described as a comparative example.

[Existing Functional Configuration Example]

FIG. 13 is a drawing illustrating a functional configuration example ofa physical property prediction device based on the existing technique.In FIG. 13, a physical property prediction device 9 has a functionalconfiguration based on the existing technique. In the comparativeexample illustrated in FIG. 13, a prediction processing unit 90 of thephysical property prediction device 9 includes a model generator 91 anda physical property prediction unit 95.

The model generator 91 is a processing unit that generates a regressionmodel 83 f and includes a substructure number calculating unit 93 b anda regression model construction unit 93 f. The substructure numbercalculating unit 93 b obtains the repeating unit from the polymerstructure and physical property DB 31 and stores the number ofsubstructures 83 b in the storage unit 130. The regression modelconstruction unit 93 f constructs the regression model 83 f byperforming a regression analysis using the number of substructures 83 band an experimental value obtained from the polymer structure andphysical property DB 31, and stores the regression model 83 f in thestorage unit 130.

The physical property prediction unit 95 is a processing unit thatpredicts the physical property value by applying the polymer structure 6a desired to be processed by a user to the regression model 83 f, andincludes a polymer structure input unit 97 a and a polymer physicalproperty prediction unit 97 b. The polymer structure input unit 97 astores the polymer structure 6 a in the storage unit 130 when receivingthe polymer structure 6 a. The polymer physical property prediction unit97 b obtains the number of substructures of the polymer structure 6 aand applies the obtained number of substructures to the regression model83 f to obtain the predicted physical property value 6 b. The physicalproperty value 6 b is presented to the user.

[Verification of the Prediction Accuracy]

Next, results of verifying the prediction accuracy of the functionalconfiguration example as a comparative example and the first to fourthfunctional configuration examples will be described. In each functionalconfiguration example, the inventors have verified the predictionaccuracy when the refractive index is predicted.

EXAMPLE 1

In Example 1, the second functional configuration example (FIG. 10) inwhich the model structure generator 43 a and the dimension reductionunit 43 e are removed was used for the verification. In Example 1, themodel structure 33 a is not generated. In addition, the regressionanalysis is performed only once by the regression model constructionunit 43 f.

In the above-described second functional configuration example,structures of 51 general infinite linear polymers, such as polyethyleneand polypropylene, (i.e., repeated units) and experimental values of therefractive index were used as training data. ECFP4 was used as thestructure descriptor. Here, ECFP4 indicates ECFP having a radius of 4and 1024 dimensions. Hereinafter, ECPF4 will be referred to as an ECFPdescriptor.

The ECFP descriptor is represented by a vector, each dimensionrepresents a substructure classification, and a value of each dimensioncorresponds the number of substructures. Software such as RDKit was usedto generate the ECFP descriptor. For convenience, the ECFP Descriptor isdefined as a substructure number descriptor. Then, a substructure numberdensity descriptor is obtained by dividing each element of thesubstructure number descriptor by the number of constituent atoms.

As a regression method, the Gaussian process regression was used. Theregression model 33 f was constructed using the substructure numberdensity descriptor as an explanatory variable and the experimental valueof the refractive index as an objective variable.

A data set was divided into a training data set and a verification dataset by leave-one-out cross-verification, a regression model wasconstructed after hyperparameter tuning was performed using the trainingdata set, and the refractive index of a polymer that was not learned waspredicted using the verification data set. The coefficient ofdetermination R² and the root mean squared error (RMSE) were used asevaluation measures of the prediction accuracy. There are variousdefinition equations of R², but the following equation was used thistime.

$\begin{matrix}{R^{2} \equiv {1 - \frac{\sum_{i}\left( {y_{i} - f_{i}} \right)^{2}}{\sum_{i}\left( {y_{i} - \overset{\_}{y}} \right)^{2}}}} & \left\lbrack {{Eq}.\mspace{14mu} 1} \right\rbrack\end{matrix}$

Here, y_(i) is an experimental value of an ith polymer, y_bar is ameasured average value, and f_(i) is the predicted value of the ithpolymer.

In the description above, the number of constituent atoms corresponds tothe number of atoms of the repeating unit, the substructure numberdescriptor corresponds to the numbers of substructures 33 b, and thesubstructure number density descriptor corresponds to the substructurenumber densities 33 d.

EXAMPLE 2

In Example 2, the third functional configuration example (FIG. 11) inwhich the model structure generator 43 a was removed was used for theverification. Since the model structure 33 a was not generated, thesubstructure number density 33 d was obtained from the repeating unit ofthe polymer structure and physical property DB 31. The regressionanalysis was performed twice by the dimension reduction unit 43 e andthe regression model construction unit 43 f.

Before constructing the regression model 33 f, the dimension reductionunit 43 e constructs a Lasso regression model (corresponding to thetemporal regression model) from the substructure number density 33 d andthe experimental value of the physical property of the polymer obtainedfrom the polymer structure and physical property DB 31, and performs thedimension reduction by excluding variables with coefficients of 0. Inthe regression model construction unit 43 f, the regression model 33 fwas constructed by the Gaussian process regression in a manner similarto Example 1. Then, the prediction accuracy of the refractive index wasverified by a method similar to the method of Example 1.

EXAMPLE 3

In Example 3, the fourth functional configuration example (FIG. 12) inwhich the dimension reduction unit 43 e is removed was used for theverification. The important substructure 33 e was not obtained. Theregression model 33 f with the number of dimensions of the substructuresnumber densities 33 d was constructed.

First, after 10 repeating units were generated as the model structure 33a, the substructure number descriptor and the number of atoms werecalculated, and the substructure number density descriptor wascalculated. After the substructure number density descriptor wascalculated, a predicted value of the refractive index was obtained by amethod similar to the method of Example 1, and the prediction accuracywas verified.

EXAMPLE 4

In Example 4, the first functional configuration example (FIG. 3)including the important substructure 33 e and the dimension reductionunit 43 e was used for the verification.

First, after 10 repeating units were. generated as the model structure33 a, the substructure number descriptor and the number of atoms werecalculated, and the substructure number density descriptor wascalculated. In addition, the dimension reduction unit 43 e constructed aLasso regression model (corresponding to the temporal regression model)and constructed the regression model 33 f by the Gaussian processregression after reducing the dimensions by excluding variables withcoefficients of 0. Then, the prediction accuracy of the refractive indexwas verified by a method similar to the method of Example 1.

COMPARATIVE EXAMPLE 1

In Comparative example 1, the existing functional configuration example(FIG. 13) in which only the substructure number calculating unit 93 band the regression model construction unit 93 f obtains the regressionmodel 83 f was used for the verification.

The substructure number calculating unit 93 b obtained the regressionmodel 83 f from the number of substructures 83 b (i.e., the substructurenumber descriptor) obtained using the repeating unit and theexperimental value of the refractive index. The regression model 83 fwas constructed using the Gaussian process regression in a mannersimilar to the manner of Example 1. The predictive accuracy was verifiedin a manner similar to the manner of Example 1.

FIG. 14 is a drawing illustrating an example of evaluation results. FIG.14 illustrates the evaluation results of the prediction accuracy of therefractive index evaluated for each of Comparative example 1 andExamples 1 to 4 as described above. Results of R² and RMSE are shown asthe evaluation results.

In the evaluation result of R², compared with “−0.063” of ComparativeExample 1, results of “0.890” in Example 1, “0.900” in Example 2,“0.932” in Example 3, and “0.950” in Example 4 are shown. The closer to“1” R² is, the higher the prediction accuracy becomes. Thus, from theevaluation results, it is found that the prediction accuracy can besignificantly improved in any of the functional configuration examplesaccording to the present embodiment.

In RMSE, compared with “0.084” in Comparative example 1, results of“0.027” in Example 1, “0.026” in Example 2, “0.021” in Example 3, and“0.018” in Example 4 are shown. The closer to “0” RMSE is, the higherthe prediction accuracy becomes. Thus, it is found that the predictionaccuracy can be significantly improved in all the functionalconfiguration examples according to the present embodiment in theevaluation results.

From such evaluation results, it can be said that the substructurenumber density 33 d, which the inventors have focused on, greatlycontributes to the improvement of the prediction accuracy in the presentembodiment.

FIG. 15 illustrates an example of results of an evaluation performed bychanging the physical property and the number of data in the data set ina manner similar to the manner of Example 4. FIG. 15 illustratesevaluation results of physical properties of Examples 5 to 9 performedby a respective number of data in a manner similar to the manner ofExample 4. Results of R² are shown as the evaluation results.

In the results of R², results of a refractive index, a glass transitionpoint [k], a band gap [eV], a relative permittivity, a coefficient oflinear expansion [10⁻⁵/K], and a density [g/cm³] are greater than orequal to 0.6. Thus, it is found that the prediction accuracy can besignificantly improved.

Since the physical property value can be predicted in advance using thephysical property prediction device 100, the polymer structure 6 a to bemanufactured can be accurately selected. Thus, it is possible to reducewaste of costs, time, and so on related to polymer manufacturing.

As described above, according to the present embodiment, the physicalproperties of the polymer can be accurately predicted by generating thestructure descriptor based on the polymer structure.

Next, a manufacturing method of a polymer using the desired polymerstructure 6 a and the predicted physical property will be described.FIG. is a drawing illustrating an example of a manufacturing method ofthe polymer.

From FIG. 16, the manufacturing method of the polymer in the presentembodiment includes a step of obtaining a polymer structure having aproperty value predicted by the polymer physical property predictionmethod according to the present embodiment (S601) and a step ofpreparing raw materials for forming the polymer structure (S602), and astep of causing the raw materials to react to obtain a polymerrepresented by the polymer structure (S603).

In step S601, a polymer structure predicted to have a predeterminedphysical property value is appropriately selected from the input polymerstructures. For example, a polymer structure whose refractive index iswithin a predetermined range may be selected from the input polymerstructures.

In step S602, raw materials of monomers, oligomers, and the like areprepared as raw materials for forming the polymer structure selected instep S601. For example, if the selected polymer structure ispolyethylene, ethylene is prepared.

In step S603, the raw materials prepared in S602 are caused to react toobtain a polymer represented by the polymer structure selected in S601.For example, ethylene is polymerized to form polyethylene.

The present invention is not limited to the specifically disclosedembodiments and various variations and modifications can be made withoutdeparting from the scope of the claims.

The present application is based on and claims priority to JapanesePatent Application No. 2018-043171, filed on Mar. 9, 2018, the entirecontents of which are hereby incorporated herein by reference.

What is claimed is:
 1. A polymer physical property prediction devicecomprising: a processor; and a memory storing program instructions thatcause the processor to read a structural unit from a storage unit anduse the structural unit to calculate numbers each indicating how manysubstructures are in a polymer, the polymer being formed of repetitionof the structural unit; calculate a number indicating how many atoms arein the structural unit; calculate number densities of the substructuresfrom the numbers of substructures and the number of atoms in thestructural unit; construct a regression model that predicts a physicalproperty value by using an experimental value of a physical property ofthe polymer and the number densities of the substructures, theexperimental value being obtained from the storage unit; input a polymerstructure of which the physical property value is to be predicted; andpredict the physical property value corresponding to the input polymerstructure by using the regression model.
 2. The polymer physicalproperty prediction device as claimed in claim 1, wherein the programinstructions cause the processor to further classify the substructuresof the polymer using an algorithm, and the calculated numbers ofsubstructures each indicate a number of each corresponding classifiedsubstructure present in the polymer.
 3. The polymer physical propertyprediction device as claimed in claim 1, wherein the programinstructions cause the processor to further remove part of thecalculated number densities of the substructures.
 4. The polymerphysical property prediction device as claimed in claim 1, wherein theprogram instructions cause the processor to further generate a modelstructure by repeatedly using the structural unit obtained from thestorage unit.
 5. The polymer physical property prediction device asclaimed in claim 1, wherein the program instructions cause the processorto further generate a model structure by repeatedly using the structuralunit obtained from the storage unit, and remove part of a structuredescriptor representing the calculated number densities of thesubstructures, wherein the numbers of substructures and the number ofatoms are calculated from the generated model structure.
 6. The polymerphysical property prediction device as claimed in claim 1, wherein theprogram instructions cause the processor to calculate the numbers ofsubstructures and the number of atoms in the polymer structure by usingthe input polymer structure, calculate the number densities of thesubstructures from the calculated numbers of substructures and thecalculated number of atoms, and apply the calculated number densities tothe constructed regression model to predict the physical property valueof the polymer structure.
 7. The polymer physical property predictiondevice as claimed in claim 3, wherein the program instructions cause theprocessor to calculate the numbers of substructures and the number ofatoms of the polymer structure by using the input polymer structure,calculate the number densities of the substructures from the calculatednumbers of substructures and the calculated number of atoms, remove partof the calculated number densities of the substructures, and apply thenumber densities whose part is removed to the constructed regressionmodel to predict the physical property value of the polymer structure.8. The polymer physical property prediction device as claimed in claim4, wherein the program instructions cause the processor to generate themodel structure by repeatedly using the input polymer structure,calculate the numbers of substructures and the number of atoms in themodel structure, calculate the number densities of the substructuresfrom the calculated numbers of substructures and the calculated numberof atoms, and apply the calculated number densities to the constructedregression model to predict the physical property value of the polymerstructure.
 9. The polymer physical property prediction device as claimedin claim 5, wherein the program instructions cause the processor togenerate the model structure by repeatedly using the input polymerstructure, calculate the numbers of substructures and the number ofatoms in the model structure, calculate the number densities of thesubstructures from the calculated numbers of substructures and thecalculated number of atoms, remove the part of the structure descriptorrepresenting the calculated number densities of the substructures, andapply the number densities whose part is removed to the constructedregression model to predict the physical property value of the polymerstructure.
 10. A non-transitory computer-readable recording mediumhaving stored therein a polymer physical property prediction program forcausing a computer to execute a process comprising: reading a structuralunit from a storage unit and using the structural unit to calculatenumbers each indicating how many substructures are in a polymer, thepolymer being formed of repetition of the structural unit; calculating anumber indicating how many atoms are in the structural unit; calculatingnumber densities of the substructures from the numbers of substructuresand the number of atoms in the structural unit; constructing aregression model that predicts a physical property value by using anexperimental value of a physical property of the polymer and the numberdensities of the substructures, the experimental value being obtainedfrom the storage unit; inputting a polymer structure of which thephysical property value is to be predicted; and predicting the physicalproperty value corresponding to the input polymer structure by using theregression model.
 11. The recording medium as claimed in claim 10,wherein the predicting the physical property value further includescalculating the numbers of substructures and the number of atoms in thepolymer structure by using the input polymer structure, calculating thenumber densities of the substructures from the calculated numbers ofsubstructures and the calculated number of atoms, and applying thecalculated number densities to the regression model to predict thephysical property value of the polymer structure.
 12. A polymer physicalproperty prediction method executed by a computer, the methodcomprising: reading a structural unit from a storage unit and using thestructural unit to calculate numbers each indicating how manysubstructures are in a polymer, the polymer being formed of repetitionof the structural unit; calculating a number indicating how many atomsare in the structural unit; calculating number densities of thesubstructures from the numbers of substructures and the number of atomsin the structural unit; constructing a regression model that predicts aphysical property value by using an experimental value of a physicalproperty of the polymer and the number densities of the substructures,the experimental value being obtained from the storage unit; inputting apolymer structure of which the physical property value is to bepredicted; and predicting the physical property value corresponding tothe input polymer structure by using the regression model.
 13. Thepolymer physical property prediction method as claimed in claim 12,wherein the predicting the physical property value further includescalculating the numbers of substructures and the number of atoms in thepolymer structure by using the input polymer structure, calculating thenumber densities of the substructures from the calculated numbers ofsubstructures and the calculated number of atoms, and applying thecalculated number densities to the regression model to predict thephysical property value of the polymer structure.
 14. A polymermanufacturing method comprising: obtaining a polymer structure having aphysical property predicted by the polymer physical property predictionmethod as claimed in claim 12; preparing materials for forming thepolymer structure; and obtaining a polymer represented by the polymerstructure by reacting the materials.
 15. The polymer physical propertyprediction device as claimed in claim 1, wherein the physical propertyvalue is a value of a refractive index or a value of a glass transitionpoint.
 16. The polymer physical property prediction device as claimed inclaim 1, wherein the physical property value is a value of a band gap, avalue of a relative permittivity, a value of a coefficient of linearexpansion, or a value of a density.